Data visualization is all about communication.

Just like in graphics design, less is more. To get a good graphics remove all excess ink.

• What do I want to say?
• What do I need to say?
• What part of my information is redundant?
• What is the standard way of displaying the information in my field?

Resist the temptation of showing every bit of data. If necessary, put it in the supplementary materials.

p <- mtcars %>% group_by(cyl) %>% 
      summarise(mean_mpg=mean(mpg)) %>%
      mutate(cyl=factor(cyl)) %>% 
      ggplot(aes(x=cyl, y=mean_mpg, fill=cyl))
p + geom_bar(stat="identity", mapping=aes(fill=cyl)) + 
  theme(axis.line=element_line(size=1, arrow=arrow(length=unit(0.1, "inches"))))

“Clutter and confusion are failures of design, not attributes of information.” (Tufte)

boxplot(hwy ~ class, data=mpg)

toupper1st <- function(x) 
  paste0(toupper(substring(x, 1, 1)), substring(x, 2))
mpg %>% mutate(class=toupper1st(class)) %>% 
  ggplot(aes(class, hwy)) + geom_tufteboxplot() + theme_tufte() + xlab("") + 
  theme(axis.text=element_text(size=14), axis.title.y=element_text(size=18, margin=margin(0,20,0,0))) +
  theme(axis.ticks.x=element_blank()) +
  theme(axis.text.x=element_text(margin=margin(30,0,0,0)))

p <- list()
p$p1 <- ggplot(mtcars, aes(x=disp, y=hp, color=factor(cyl))) + geom_point() 
p$p2 <- ggplot(mtcars, aes(x=disp, y=hp, color=factor(cyl))) + geom_point() + 
  theme_par()
p$p3 <- ggplot(mtcars, aes(x=disp, y=hp, color=factor(cyl))) + geom_point() + 
  theme_cowplot()
p$p4 <- ggplot(mtcars, aes(x=disp, y=hp, color=factor(cyl))) + geom_point() + 
  theme_tufte()

p <- map(p, ~ . + theme(plot.margin=margin(20, 0, 0, 0)))
plot_grid(plotlist=p, labels=c("Default", "Par", "Cowplot", "Tufte"))

“Above all else show the data.” (Tufte)

• Bar charts have their purpose: showing proportions or absolute quantities (1 value per bar)
• Y axis must always start at 0, because bar charts communicate with the bar surface area
• Bar charts are often misused to show sample means and sample spread; they should be replaced by box plots, violin plots or dot plots.

(demo)

Editorial. "Kick the bar chart habit." Nature Methods 11 (2014): 113.

• Pie charts are bad at communicating information, just don't use them
• Don't even mention 3D pie charts
• There are tons of alternatives to pie charts

• What is the function of color on the plot?
• Does the color help or distract?

There are many ways to represent colors. In R, we most frequently use the RGB scheme in which each color is composed of three values for each of the three colors: red, green and blue.

One way is to choose values between 0 and 1; another, between 0 and 255. The latter can be represented using hexadecimal notation, in which the value goes from 0 to FF (15 * 16 + 15 = 255). This is a very common notation, used also in HTML:

• "#FF0000" or c(255, 0, 0): red channel to the max, blue and green to the minimum. The result is color red.
• "#00FF00": bright green
• "#000000": black
• "#FFFFFF": white

• To get the color from numbers in 0…1 range:

  rgb(0.5, 0.7, 0) # returns "#80B300"

• To get the color from numbers in 0…255 range:

  rgb(255, 128, 0, maxColorValue=255)

Useful way to handle large numbers of data points. #FF000000: fully transparent; #FF0000FF: fully opaque.

x <- rnorm(10000)
y <- x + rnorm(10000)
p1 <- ggplot(NULL, aes(x=x, y=y)) + geom_point() + 
  theme_tufte() + theme(plot.margin=unit(c(2,1,1,1), "cm"))
p2 <- ggplot(NULL, aes(x=x, y=y)) + geom_point(color="#6666661F") + 
  theme_tufte() + theme(plot.margin=unit(c(2,1,1,1),"cm"))
plot_grid(p1, p2, labels=c("Black", "#6666661F"))

Useful way to handle large numbers of data points. #FF000000: fully transparent; #FF0000FF: fully opaque.

There are several other representations of color space, and they do not give exactly the same results. Two common representations are HSV and HSL: Hue, Saturation and Value, and Hue, Saturation and Luminosity.

There are many packages to help you manipulate the colors using hsl and hsv. For example, my package plotwidgets allows you to change it using the HSL model.

library(plotwidgets)
## Now loop over hues
pal <- plotPals("zeileis")
v <- c(10, 9, 19, 9, 15, 5)

a2xy <- function(a, r=1, full=FALSE) {
  t <- pi/2 - 2 * pi * a / 360
  list( x=r * cos(t), y=r * sin(t) )
}

plot.new()
par(usr=c(-1,1,-1,1))
hues <- seq(0, 360, by=30)
pos <- a2xy(hues, r=0.75)
for(i in 1:length(hues)) {
  cols <- modhueCol(pal, by=hues[i])
  wgPlanets(x=pos$x[i], y=pos$y[i], w=0.5, h=0.5, v=v, col=cols)
}

pos <- a2xy(hues[-1], r=0.4)
text(pos$x, pos$y, hues[-1])

There are many packages to help you manipulate the colors using hsl and hsv. For example, my package plotwidgets allows you to change it using the HSL model.

It is not easy to get a nice combination of colors (see default plot in ggplot2 to see how not to do it).

There are numerous palettes in numerous packages. One of the most popular is RColorBrewer. You can use it with both base R and ggplot2.

library(RColorBrewer)
par(mar=c(0,4,0,0))
display.brewer.all()

par(mar=c(0,4,0,0))
display.brewer.all(colorblindFriendly=T)

data("iris")

The use of multiple measurements in taxonomic problems as an example of linear discriminant analysis. Fisher 1936

ggplot(iris, aes(x=Sepal.Length, y=Sepal.Width, color=Species)) + 
  geom_point(size=4) + theme_tufte() + 
  theme(axis.title.y=element_text(margin=margin(0,10,0,0)), 
        axis.title.x=element_text(margin=margin(10, 0, 0, 0)))

ggplot(iris, aes(x=Sepal.Length, y=Sepal.Width, color=Species)) + 
  geom_point(size=4) + scale_color_brewer(palette="Dark2")  + theme_tufte() + 
  theme(axis.title.y=element_text(margin=margin(0,10,0,0)), 
        axis.title.x=element_text(margin=margin(10, 0, 0, 0)))

ggplot(iris, aes(x=Sepal.Length, y=Sepal.Width, color=Species)) + 
  geom_point(size=4) + scale_color_brewer(palette="Paired") + theme_tufte() + 
  theme(axis.title.y=element_text(margin=margin(0,10,0,0)), 
        axis.title.x=element_text(margin=margin(10, 0, 0, 0)))

ggplot(iris, aes(x=Sepal.Length, y=Sepal.Width, color=Species)) + 
  geom_point(size=4) + scale_color_brewer(palette="Set2") + theme_tufte() + 
  theme(axis.title.y=element_text(margin=margin(0,10,0,0)), 
        axis.title.x=element_text(margin=margin(10, 0, 0, 0)))